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            <h1 class="heading text-white mb-1 font-weight-bold">
              Error Analysis
            </h1>
            <h2 class="text-white subheading mb-3">
              Identify & Diagnose Errors <br />Build Responsibly
            </h2>
            <p class="text-white mb-3 mb-md-4">
              A toolkit to help analyze and improve model accuracy.
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                  <a class="nav-link active" data-toggle="pill" href="#identify"
                    >Identify</a
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                  <a class="nav-link" data-toggle="pill" href="#diagnose"
                    >Diagnose</a
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                  <p class="mb-0 text-darkblue">
                    Identify cohorts with high error rate versus benchmark and
                    visualize how the error rate distributes.
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                <div id="diagnose" class="container tab-pane fade px-2">
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                    Diagnose root causes by visually diving deeper into the
                    characteristics of data and models.
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        <!-- ========= WHY ERROR ANALYSIS SECTION START ========== -->
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              <h3 class="text-darkblue mb-3 font-weight-bold">
                Why Error Analysis?
              </h3>
              <p class="text-lightblue">
                Error Analysis is a Responsible AI toolkit that enables you to
                get a deeper understanding of machine learning model errors.
                When evaluating a machine learning model, aggregate accuracy is
                not sufficient and single-score evaluation may hide important
                conditions of inaccuracies. Use Error Analysis to identify
                cohorts with higher error rates and diagnose the root causes
                behind these errors.
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                <h4 class="text-darkblue font-weight-bold">Evaluate Cohorts</h4>
                <p class="text-lightblue mb-0">
                  Learn how errors distribute across different cohorts at
                  different levels of granularity
                </p>
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                <img src="./img/icon-2.svg" alt="Explore predictions icon" />
                <h4 class="text-darkblue font-weight-bold">
                  Explore Predictions
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                <p class="text-lightblue mb-0">
                  Use built-in interpretability features or combine with
                  InterpretML for boosted debugging capability
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                <h4 class="text-darkblue font-weight-bold">
                  Interactive Dashboard
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                <p class="text-lightblue mb-0">
                  View customizable pre-built visuals to quickly identify errors
                  and diagnose root causes
                </p>
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        <!-- ========= WHY ERROR ANALYSIS SECTION ENDS ========== -->

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              <h3 class="text-white mb-4 mb-md-5 font-weight-bold">
                How Error Analysis Works
              </h3>
              <h4 class="text-white font-weight-bold">Identification</h4>
              <p class="text-white px-0 px-md-5">
                Error Analysis identifies cohorts of data with higher error rate
                than the overall benchmark. These discrepancies might occur when
                the system or model underperforms for specific demographic
                groups or infrequently observed input conditions in the training
                data.
              </p>
            </div>
            <div class="text-white text-center">
              <h5 class="mb-4">Different Methods for Error Identification</h5>
            </div>
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                <p class="mb-2 font-weight-bold">Decision Tree</p>
                <p>
                  Discover cohorts with high error rates across multiple
                  features using the binary tree visualization. Investigate
                  indicators such as error rate, error coverage, and data
                  representation for each discovered cohort.
                </p>
              </div>
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                  <img src="./img/identification1.jpg" />
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                <p class="mb-2 font-weight-bold">Error Heatmap</p>
                <p>
                  Once you form hypotheses of the most impactful features for
                  error, use the error heatmap to further investigate how one or
                  two input features impact the error rate across cohorts.
                </p>
              </div>
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                <div class="img-box-wrapper">
                  <img src="./img/identification2.jpg" />
                </div>
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              <h4>Diagnosis</h4>
              <p class="mb-4 pb-2 px-0 px-md-5">
                After identifying cohorts with high error rates, Error Analysis
                enables you to understand the reasons behind the error rates so
                you can take corrective actions.
              </p>
            </div>

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                <h5 class="mb-4">Different Methods for Error Diagnosis</h5>
              </div>
              <div class="col-12 col-md-6 order-md-2">
                <p class="mb-2 font-weight-bold">Data Exploration</p>
                <p>
                  Explore dataset statistics and feature distributions. Compare
                  cohort statistics with other cohorts or to benchmark.
                  Investigate whether certain cohorts are underrepresented or
                  their feature distribution is significantly different from the
                  overall data.
                </p>
              </div>
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                  <img src="./img/diagnosis1.jpg" />
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                <p class="mb-2 font-weight-bold">Global Explanation</p>
                <p>
                  Explore the most important features that impact the model
                  global explanation of a selected cohort. Understand how values
                  of features impact model predictions. Compare explanations of
                  your selected cohorts with other cohorts or benchmark.
                </p>
              </div>
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                  <img src="./img/diagnosis2.jpg" />
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                <p class="mb-2 font-weight-bold">Local Explanation</p>
                <p>
                  Analyze the selected cohort's datapoints in instance view,
                  divided by correct or incorrect predictions. Visually examine
                  for missing features or label noises as potential causes for
                  incorrect predictions. Understand which features have the most
                  impact to each data point's predictions. Observe individual
                  conditional expectation plots.
                </p>
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                  <img src="./img/diagnosis3.jpg" />
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              <div class="col-12 col-md-6 order-md-2">
                <p class="mb-2 font-weight-bold">What-if Analysis</p>
                <p>
                  Apply changes to feature values of a selected data point and
                  observe the resulting change to the prediction.
                </p>
              </div>
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                  <img src="./img/what-if.jpg" />
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            <h3 class="text-darkblue font-weight-bold">Getting Started</h3>
            <a
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              href="https://github.com/microsoft/responsible-ai-widgets/#getting-started"
              >Install Error Analysis</a
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                <h3 class="font-weight-bold">Contribute To Error Analysis</h3>
                <p class="mb-0">
                  We encourage you to join the effort and contribute feedback,
                  algorithms, ideas and more, so we can evolve the toolkit
                  together! Example contributions may include novel
                  visualizations, failure explanation algorithms, improved UX
                  experience, or further integration with other toolkits in
                  Responsible AI.
                </p>
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              Explore other Responsible AI toolkits
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              Assess fairness & mitigate unfairness using
              <a href="https://fairlearn.github.io/">Fairlearn</a>
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              Understand model behaviors using
              <a href="https://interpret.ml/">InterpretML</a>
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